Prevalence and demographic, socioeconomic, and behavioral risk factors of self-reported symptoms of sexually transmitted infections (STIs) among ever-married women : evidence from nationally representative surveys in Bangladesh

Sexually transmitted infections (STI) symptoms (e.g., abnormal genital discharge and genital sores/ulcers) are a major public health concern in Bangladesh because the symptoms can indicate an STI and cause sexual and reproductive health complications in women of reproductive age. To our knowledge, no study examined the prevalence and risk factors of STI symptoms using a nationally representative sample. This study investigates the prevalence of STI symptoms among ever-married women in Bangladesh and the associations of STI symptoms with various demographic, socioeconomic, and behavioral risk factors using the most recent available data (2007, 2011, and 2014) of the Bangladesh Demographic and Health Surveys (BDHS). The BDHS employs a two stage stratified sampling technique. The analytic sample comprised 41,777 women of reproductive age (15–49 years). Outcome variables included STI symptoms: abnormal genital discharge and genital sores/ulcers. Multivariate logistic regression was employed to find the adjusted odds ratio with a 95% confidence interval to assess the associations of outcome measures with explanatory variables. The study found that the prevalence of abnormal genital discharge and genital sores/ulcers among ever-married women aged 15–49 years was 10% and 6%, respectively. Multivariable analysis revealed that for women aged 25–34 years, those who used contraceptives and married earlier had an increased likelihood of STI symptoms. Furthermore, women from the wealthiest wealth quintile and couple’s joint decision-making were less likely to have STI symptoms. Findings have implications for interventions efforts aiming to improve women’s sexual and reproductive health in Bangladesh

An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification

Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available `PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.Comment: Accepted in ECCE 2023, 6 pages, 6 figures, 4 table

In vivo anxiolytic and in vitro anti-inflammatory activities of water-soluble extract (WSE) of Nigella sativa (L.) seeds

The WSE is a highly polar, gummy and mucilaginous bioactive content of the Nigella sativa (L.) seeds. This study reports the anxiolytic and anti-inflammatory effects of WSE investigated using Elevated Plus Maze (EPM) and Hole-Board Test (HBT) in adult mice and human RBCs haemolysis inhibition and protein denaturation respectively. The oral WSE treatment (100 & 200 mg/kg b.w/day) for 72 hours has exhibited slightly better anxiolytic effect (p < 0.05) through the time span (92.33 & 93.33 s) spent in the opened arms of EPM vs. diazepam (1 mg/kg b.w i.p/day; 69.33 s). In HBT, only WSE (200 mg/kg b.w/day) has shown a promising number of mean head pokes (13.27 times/min) vs. diazepam (12.87 times/min). The WSE (62.5-500 mg/mL) exposure has exhibited 40.14-72.18% protection against lysis of RBCs vs. aspirin (57.04-71.48%) whilst 62.67-67.66% inhibition of protein denaturation vs. diclofenac sodium (43.11-80.64%). The current findings suggested WSE has promising anxiolytic and anti-inflammatory activities

Assessing performance of the Healthcare Access and Quality Index, overall and by select age groups, for 204 countries and territories, 1990-2019: a systematic analysis from the Global Burden of Disease Study 2019

Background: Health-care needs change throughout the life course. It is thus crucial to assess whether health systems provide access to quality health care for all ages. Drawing from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019), we measured the Healthcare Access and Quality (HAQ) Index overall and for select age groups in 204 locations from 1990 to 2019. Methods: We distinguished the overall HAQ Index (ages 0–74 years) from scores for select age groups: the young (ages 0–14 years), working (ages 15–64 years), and post-working (ages 65–74 years) groups. For GBD 2019, HAQ Index construction methods were updated to use the arithmetic mean of scaled mortality-to-incidence ratios (MIRs) and risk-standardised death rates (RSDRs) for 32 causes of death that should not occur in the presence of timely, quality health care. Across locations and years, MIRs and RSDRs were scaled from 0 (worst) to 100 (best) separately, putting the HAQ Index on a different relative scale for each age group. We estimated absolute convergence for each group on the basis of whether the HAQ Index grew faster in absolute terms between 1990 and 2019 in countries with lower 1990 HAQ Index scores than countries with higher 1990 HAQ Index scores and by Socio-demographic Index (SDI) quintile. SDI is a summary metric of overall development. Findings: Between 1990 and 2019, the HAQ Index increased overall (by 19·6 points, 95% uncertainty interval 17·9–21·3), as well as among the young (22·5, 19·9–24·7), working (17·2, 15·2–19·1), and post-working (15·1, 13·2–17·0) age groups. Large differences in HAQ Index scores were present across SDI levels in 2019, with the overall index ranging from 30·7 (28·6–33·0) on average in low-SDI countries to 83·4 (82·4–84·3) on average in high-SDI countries. Similarly large ranges between low-SDI and high-SDI countries, respectively, were estimated in the HAQ Index for the young (40·4–89·0), working (33·8–82·8), and post-working (30·4–79·1) groups. Absolute convergence in HAQ Index was estimated in the young group only. In contrast, divergence was estimated among the working and post-working groups, driven by slow progress in low-SDI countries. Interpretation: Although major gaps remain across levels of social and economic development, convergence in the young group is an encouraging sign of reduced disparities in health-care access and quality. However, divergence in the working and post-working groups indicates that health-care access and quality is lagging at lower levels of social and economic development. To meet the needs of ageing populations, health systems need to improve health-care access and quality for working-age adults and older populations while continuing to realise gains among the young. Funding: Bill & Melinda Gates Foundation

GaitGCN++: Improving GCN-based gait recognition with part-wise attention and DropGraph

Gait recognition is becoming one of the promising methods for biometric authentication owing to its self-effacing nature. Contemporary approaches of joint position-based gait recognition generally model gait features using spatio-temporal graphs which are often prone to overfitting. To incorporate long-range relationships among joints, these methods utilize multi-scale operators. However, they fail to provide equal importance to all joint combinations resulting in an incomplete realization of long-range relationships between joints and important body parts. Furthermore, only considering joint coordinates may fail to capture discriminatory information provided by the bone structures and motion. In this work, a novel multi-scale graph convolution approach, namely ‘GaitGCN++’, is proposed, which utilizes joint and bone information from individual frames and joint-motion data from consecutive frames providing a comprehensive understanding of gait. An efficient hop-extraction technique is utilized to understand the relationship between closer and further joints while avoiding redundant dependencies. Additionally, traditional graph convolution is enhanced by leveraging the ‘DropGraph’ regularization technique to avoid overfitting and the ‘Part-wise Attention’ to identify the most important body parts over the gait sequence. On the benchmark gait recognition dataset CASIA-B and GREW, we outperform the state-of-the-art in diversified and challenging scenarios

Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification

To ensure global food security and the overall profit of stakeholders, the importance of correctly detecting and classifying plant diseases is paramount. In this connection, the emergence of deep learning-based image classification has introduced a substantial number of solutions. However, the applicability of these solutions in low-end devices requires fast, accurate, and computationally inexpensive systems. This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with illumination correction for improved classification. Our system extracts features using a combined model consisting of a pretrained MobileNetV2 architecture and a classifier network for effective prediction. Traditional augmentation approaches are replaced by runtime augmentation to avoid data leakage and address the class imbalance issue. Evaluation on tomato leaf images from the PlantVillage dataset shows that the proposed architecture achieves 99.30% accuracy with a model size of 9.60MB and 4.87M floating-point operations, making it a suitable choice for real-life applications in low-end devices. Our codes and models are available at https://github.com/redwankarimsony/project-tomato.Comment: 18 pages, 13 figures, 5 tables, Accepted in IEEE Acces

Potential insulin-like and insulin-sensitizing activity on 3T3-L1 adipocytes by terpeniods isolated from Tetracera indica

Aerial parts of Tetracera indica Merr. (Dilleniaceae) are traditionally used to treat diabetes in Malaysia. T. indica has been reported to contain terpenoids namely stigmasterol and betulinic acid as major constituents. Hence, the aim of this study was to evaluate in vitro antidiabetic potential of these two major terpenoids on 3T3-L1 pre-adipocytes and adipocytes. Cytotoxicity test was performed through MTT assay on 3T3-L1 pre-adipocytes to determine the safe dose for further in vitro antidiabetic evaluation. 2-NBDG glucose uptake test on mature 3T3-L1 adipocytes with the isolated terpenoids was carried out to confirm their antidiabetic effect. Insulin and rosiglitazone were taken as control and positive control, however, untreated cells with normal media (DMEM) was taken as negative control, respectively. Ethanol extract and its ethyl acetate fraction displayed the most significant antidiabetic effect on 3T3-L1 pre-adipocytes by showing their good ability to induce adipogenesis. Upon repeated silica gel column chromatography, ethyl acetate fraction afforded stigmasterol and betulinic acid whose structures were elucidated through 1H-and 13C-NMR spectroscopy. Both compounds showed significant glucose uptake activity during fluorescence glucose uptake test by 2-NBDG (fluorescent glucose analogue) on mature adipocytes by displaying higher 2-NBDG uptake than insulin at their highest concentrations. Furthermore, both terpenoids showed more or less similar 2-NBDG uptake like rosiglitazone. This clearly proves their insulin-sensitizing ability. Our study suggests strong antidiabetic potential of isolated terpenoids in terms of insulin-like and insulin-sensitizing abilities

A conformational isomer of soulattrolide from the stem bark of calophyllum symingtonianum and its antibacterial activity

Callophylum symingtonianum (Guttiferae), an evergreen broad-leaved tree that usually grows in hill forests, can be found distributed in the Malay Peninsula. The barks, leaves, flowers and seeds is often used medicinally to treat diarrhea and rheumatism. In the present study, we isolated two inophyllum type coumarins, 12-O-ethylinophyllum D (1) and iso-soulattrolide (2) from the stembarks of C. symingtonianum together with their antibacterial activity. The compounds were isolated by chromatographic methods on a silica gel. The structures were established by spectroscopic methods including UV, IR, (1D and 2D) NMR and mass spectrometry as well as by comparison with several literature sources. The antibacterial activity of those compounds was tested using a disc-diffusion assay against Staphylococcus aureus, Bacillus cereus, Escherichia coli and Pseudomonas aeruginosa. Both compound exhibited mild inhibition against P. aeruginosa with both 111 µg/ml MIC value. Compound 2 also inhibits S. aureus with 25 µg/ml MIC value